Not in Education, Employment or Training: Causes

Not in Education, Employment or
Training: Causes, Characteristics
of NEET-affected Youth and
Exit Strategies in Austria
working paper
ISW – IBE – JKU
A study for the
Federal Ministry of Labour, Social Affairs and Consumer Protection
(BMASK)
April 2014
Institut für Sozial- und Wirtschaftswissenschaften
imprint of/editor:
Institut für Sozial- und Wirtschaftswissenschaften
Volksgartenstraße 40
4020 Linz, Austria
Tel.: ++43/732/669273
Fax: ++43/732/669273-2889
E-Mail: wiso@isw-linz.at
Internet: www.isw-linz.at
ISBN: 978-3-901320-11-8
Not in Education, Employment or Training: Causes, Characteristics of
NEET-affected Youth and Exit Strategies in Austria
Johann Bacher*1, Dennis Tamesberger**, Heinz Leitgöb* & Thomas Lankmayer***
* University of Linz, Austria
** Upper Austrian Chamber of Labour, Austria
*** Research Institute for Vocational Training and Adult Education, Austria
April 2014
Summary
The article examines the questions, how many young people in Austria have
been affected by NEET status, which socio-structural characteristics they
display and what the causes for an increased NEET risk are. Furthermore,
the study investigates what the decisive factors are for a (successful) exit
from a NEET situation. To answer these research questions, a multiplemethod approach has been chosen, which is based on quantitative and
qualitative elements. It will be shown that in the period from 2006 to 2012
on average about 78,000 young people in Austria aged between 16 and 24
were affected by disintegration within the meaning of NEET status.
However, the number of NEET youths very much depends on economic
trends; hence it significantly increased during the crisis years of 2009 and
2010. An increased NEET risk is shared by early school leavers, (female)
youths with care responsibilities, youths of the first migration generation,
youths whose parents have a lower level of education and youths, living in
urban areas. However, many of the socio-structural characteristics do not
have a direct but only an indirect impact on the NEET status. The NEET
group is very heterogeneous and equally diverse are the causes,
requirements and problems. Early school leaving has been identified as the
main direct cause of the NEET status. In order to reduce the NEET rate, the
contribution calls for low-threshold measures and take the individual
requirements of young people into account. Other proposals include
measures for (new) target groups, measures to network and coordinate
relevant actors and measures at regional level as well as measures to
continue to further develop active labour market policy.
Key words: NEET, youth unemployment, Austria, (social) exclusion, active labour
market policy, path analysis
1
Contact: johann.bacher@jku.at
1
1. Introduction
The youth unemployment rate is only of limited information value for the labour market
situation of young people (Lassnigg 2010). According to international calculation
methods (labour force concept) (Kytir & Stadler 2004), only those persons are counted
as unemployed, who were not gainfully employed (no employment of one or more hours
a week) at the time of the survey, who are currently available for work and who are
taking active steps to find employment2. Hence, those youths, who do not or no longer
seek any employment due to poor chances labour market opportunities or those who
are not immediately available due to care responsibilities, are not perceived as being
unemployed. The international definition, which interprets unemployment very
narrowly and gainful employment very broadly, can underestimate the extent of the
problem―in particular in respect of young people. Therefore, European labour market
research and political decision-makers are focussing on the NEET indicator as a
supplement to the youth unemployment rate (Eurofound 2011). NEET stands for “not in
employment, education or training” and identifies youths, who are neither integrated in
the employment nor in the educational system and who do not take part in any
(occupational) training programme. The underlying assumption is that this indicator can
also register youths, who have already moved further away from the labour market.
Hence, in a sense, the NEET indicator also presents a measure for the social exclusion of
youths and young adults, whereby it has to be said that not all NEET youths are
threatened by social exclusion and not all socially excluded youths are captured by the
NEET indicator (see Section 3). However, the NEET indicator is better in recording the
exclusion risk than the youth unemployment rate.
Countries such as Great Britain (see e.g. Furlong 2007; Coles et al. 2010) and
Japan (see e.g. Genda 2007; Inui et al. 2007) already have a long research tradition in
respect of NEET youths. The development of the indicator in the 1980ies in Great Britain
has to be seen against the background of the labour market reforms under Margaret
Thatcher. Training and education programmes for unemployed young people were
pushed through; however, in many cases they did not match the requirements or career
aspirations of the unemployed youths. A refusal to take part in these programmes
resulted in a withdrawal of financial support and those who refused were no longer
registered as unemployed. Hence, the number of young people not being in education,
employment or training (NEET) (Furlong 2007) rose again. At the end of the 1990ies,
the British government reacted to the high number of NEET youths with its “Bridging
the Gap” Report (Social Exclusion Unit 1999). The authors of the study presented a
comprehensive explanation of social exclusion of youths in Great Britain and developed
a package of measures, which has largely been implemented.
The following activities are considered actively looking for work (Kytir & Stadler 2004, 515): contact
with job centre with the specific aim to find a job; contact with private job agencies; sending applications
to potential employers; asking friends, acquaintances, interest representatives etc. about vacancies;
placing or replying to adverts; reading job adverts; attending job interviews or taking tests; etc.
2
2
In Austria, the level of awareness concerning NEET youths is comparably low. The
first quantitative estimate of NEET youths in Austria (Bacher & Tamesberger 2011)
revealed that public confusion is great concerning the indicator’s meaningfulness and
who NEET youths actually are. Apart from an in-depth social structural description of
NEET youths, Austria is also lacking the knowledge of causes and of possible ways out of
the NEET status. The present study3 attempts to close these gaps in knowledge by
looking for empirical answers to the following research questions:
(i)
(ii)
(iii)
(iv)
How many youths in Austria are affected by NEET status?
Which socio-structural characteristics do NEET youths display?
What are the causes for an increased NEET risk?
Which are the decisive factors for a (successful) exit from the NEET situation?
The article is structured as follows: Following the description of the methodical
approach and the data basis (Section 2), the report depicts the extent of the problem in
Austria (Section 3). In doing so, the share of NEET youths aged 16 to 24 as well as the
absolute figures for the period from 2006 to 2011 has been estimated and expanded by
results for 2012. Subsequently, the NEET group is described in socio-structural terms
(Section 4) and risk factors resp. causes for a NEET situation are identified (Section 5).
Finally, ways out of a NEET situation are identified and factors, which facilitate a
successful integration in the labour market resp. in the education or training system, are
worked out (Section 6). In Section 7, based on the results, recommendations for action
are being developed.
2. Methodology
In order to cover the complexity of the phenomenon, a multi-method approach has been
chosen, which is based on quantitative and qualitative elements. In addition, we
consulted international literature and set up two workshops with practitioners, who
come into daily contact with NEET youths in their capacity as consultants, social
workers or trainers.
However, the underlying data of the quantitative analyses are based on the
quarterly conducted Austrian micro-census (for details see Kytir & Stadler 2004) during
the period from 2006 to 2011. These data were merged to a panel dataset, which
includes a total of
youths aged between 16 and 24 and which is
representative for Austria. As several persons from the previously defined target
population might live in one selected household and because disproportional sampling
The article combines individual research results of the study to support the labour market policy target
group “NEET”, which the ISW (Institute of Social and Economic Sciences) has carried out in cooperation
with the Institute of Sociology (JKU) and the IBE (Research Institute for Vocational Training and Adult
Education) and the cooperation partners in Vienna (Vienna University) and Vorarlberg
(okay.zusammenleben) on behalf of the Federal Ministry of Labour, Social Affairs and Consumer
Protection. The complete study is available under: http://www.isw-linz.at/projekt-qneetq-jugendliche.
3
3
was used for the federal states, a design effect (
taken into account (see e.g. Lee & Forthofer 2006).
;
)4 has to be
As the design of the micro-census is based on a five-panel rotation scheme (each
household selected remains in the sample for five waves, Kytir & Stadler 2004, 513),
theoretically data of five consecutive quarters are available for each participant.
Empirically, complete answers are available for 76.1 % of the youths.
Missings (panel attrition) are assumed to occur not randomly. After statistical
control of relevant third variables (gender, size of location, migration background, age
and survey period), it is less likely to meet NEET youths for follow-up interviews
(Bacher et al. 2013, 62). This result indicates a Not Missing at Random mechanism
(NMAR, see e.g. Allison 2010 for an informative overview)5. This implies an
underestimation of the NEET rate of about 0.5 %, if the rate―as it is common practice―
is calculated in cross-sectional approach on the basis of all five panel waves.
Apart from descriptive procedures, linear regression and explorative path models
(Alwin & Hauser 1975) are applied for quantitative data analysis.6 The variables
depicted in Table 1 were integrated in the path analysis as independent variables, which
influence the NEET rate. Separate models were estimated according to gender as it was
assumed that different effect chains exist for young men and women.
Whilst
stands for design effect, depicting the loss of accuracy of the given complex sample (
)
compared to a simple random sample,
designates the effective sample size. It determines how large a
sample based on a simple random sample would have to be to have the same sample accuracy as the
complex sample of size
. The three survey characteristics are in the following relation to each
⁄
other:
. In the present case, the sample accuracy corresponds to the micro-census
with
25,322 youths based on a simple random sample of
16,922 youths.
4
Similar findings have been described by Mitterndorfer et al. (2007) for unemployment and in respect of
persons who do not have Austrian citizenship.
5
As all endogenous variables are dichotomous, logistic regression models would have to be used to
analyse the effects. However, it is difficult to interpret logistic regression coefficients as path coefficients
(see Mood 2010). Apart from that, the use of linear regression models for dichotomous dependent
variables in explorative path models can be convincingly justified (see e.g. Hellevik 2009). Nevertheless, in
order to safeguard the findings from the linear regressions, logistic models are also calculated. Any
differences have been stated.
6
4
Table 1: Independent variables
Variable
Migration background
Label
MIGRA
Population of location
URBAN
Age of the youth
AGE
Citizenship
CITIZ
Early school leaving
EARLY
(Chronic) diseases and
impairments
HANDI
Care duties
CHILD3Y
Previous unemployment
experience
UNEMP
Operationalization
1 = yes (first generation),
0 = no
Note: Belonging to second generation
migrants is not known for the entire
sample period.
1 = 30,001 and more residents,
0 = other
1 = 20 to 24 years,
0 = 16 to 19 years
1 = Austrian citizenship or citizenship of
one of the EU25 countries (EU27 without
Rumania and Bulgaria),
0 = other
Note: Rumania and Bulgaria were not
counted as conventional EU countries, as
both these countries were still subject to
restricted labour market access at the
time of the survey.
1 = no compulsory schooling certificate or
graduation from a maximum one-year
BMS [Medium Level Vocational School]
0 = other
1 = yes, 0 = no
Note: Variable could only be recorded as
proxy. Persons, who are retired or cited
an illness as reason for being
unemployed, were called HANDI. The
types of illness are not known.
1 = child/children under age 3 in a
household,
0 = other
Note: Care responsibilities were recorded
on the basis of at least one child under 3
living in a household.
1 = unemployed in the previous quarter,
0 = other
Problem-focused interviews (
) with youths with NEET experiences were carried
out for the qualitative analysis in two phases. In the first phase, narrative interviews
(
) were conducted in Upper Austria, Vienna and Vorarlberg, which were later
analysed as structured individual cases in form of “causal chains” (example to follow in
5
Section 5). This material formed the basis for the derivation of a typology and served as
a template for the interview guide for the semi-standardised interviews (
)
conducted in the second phase. The second phase served for the validation and
expansion of the typology (Stadlmayr & Lankmayer 2013). The analysis was carried out
based on Grounded Theory (Strauss & Corbin 1990).
3. Youths affected by NEET
The results for the sample period show that during the timeframe between 2006 and
2012, on average 8.5 % of young people aged between 16 and 24 were in NEET state
(Table 2). In absolute terms, about 78,000 young people were in a NEET situation. By
drawing the attention to temporal variations it becomes obvious that the number of
NEET youths―similar to the number of unemployed youths―is strongly influenced by
economic trends. Due to the crisis, the NEET rate rose to about 9 % in the years 2009
and 2010. A similar high value can be observed for 2007, which, however, cannot be
explained economically. 2011 saw a noticeable reduction compared to the crises years
2009 and 2010. Compared to 2011, in 2012 the NEET rate is again on the increase,
standing at 8.3 % (about 76,000 youths), which is also partly a result of the economic
downturns in the second half of 2012.
Compared to the EU27 countries with a mean of 13.1 %, Austria has one of the
lowest NEET rates (Eurostat 2013). The value of 13.1 % refers to 15 to 24-olds; the
comparative value for Austria is stated as 6.5 % (Eurostat 2013).7
Additional ecological regression analyses (Bacher et al. 2013) show that apart
from economic trends, the NEET rate also depends on Active Labour Market Policy for
Youth in Austria. Hence, intensified Active Labour Market Policy was also a decisive
factor for the relatively low NEET rate in 2008. Thus, in spite of a positive economic
climate, the expenditure for Active Labour Market Policy for Youth increased by about 8
% from € 343,845,536.00 in 2007 to € 372,188,859.00 in 2008. 2008 also marked the
point where the “safety net“ for young people in Austria developed into a training
guarantee up to the age of 18. This shall ensure that young people, who cannot find a
company-based apprenticeship, can learn a profession within the scope of supracompany vocational training (BMASK 2012).
The NEET rate for Austria (6.5 %) calculated by Eurostat differs from our calculations (8.3 %) because of
the different age groups. Eurostat also includes 15-year olds. As most 15-year olds are still attending
school, this reduces the NEET rate. Another difference probably results from the question whether all
interviews are entered into the calculation or only the initial interview (see Section 2).
7
6
Table 2: NEET youth over the course of time (share and absolute figures)
Share of 16 to 24-olds in %
Absolute figures
Year
2006
Share
8.6
CIl(95%)
7.6
CIu(95%)
9.6
absolute
77,000
CIl(95%)
68,000
CIu(95%)
86,000
2007
9.4
8.4
10.4
85,000
76,000
94,000
2008
7.8
6.8
8.8
71,000
62,000
80,000
2009
9.0
7.9
10.1
83,000
73,000
93,000
2010
9.1
8.0
10.1
84,000
74,000
94,000
2011
7.6
6.6
8.6
70,000
61,000
79,000
2012
Average
8.3
8.5
7.2
8.1
9.4
8.9
76,000
78,000
66,000
74,000
86,000
82,000
;
;
Example: In 2012, 8.3 % of youths aged between 16 and 24 were not in education, employment, or
training (NEET). Depicted in absolute figures this equals about 76,000 young people in Austria. It is a 95
% probability that the share lies between 7.2 % (CIl95) and 9.4 % (CIu95). CIl(95%) is the lower bound of
the 95%-confidence interval, CIu(95%) is the upper bound of the 95% confidences interval.
It should be noted that based on the micro-census, the calculated number of NEET
youths represents a lower limit, because the micro-census only registers persons in
private households (Haslinger & Kytir 2006, 512). NEET youths in so-called institutional
households such as homes for younger adults, boarding schools etc. and homeless
youths cannot be taken into account. The total number of youths aged between 15 and
24 in institutional households is about 9,000 (Statistik Austria 2010). However, it is
unknown how many of these are in a NEET situation. It is also not known how many
young people in Austria are homeless. In 2010, about 6,800 homeless people in Vienna
lived in social housing. 20 %, i.e. about 1,400 persons of them aged between 18 and 29
(Riesenfelder et al. 2012, 19). One can assume that the majority of them is in a NEET
situation.
To evaluate the extent of the problem, one can refer to the proximity to the labour
market resp. the period someone remains in a NEET situation. Not all NEET youths can
be equally considered as being removed from the labour market or being in danger of
exclusion. Corresponding to the broad definition of the NEET indicator, the group of
NEET youths distinguishes itself in respect of their labour market activities. 9.2 % of
NEET youths are in a waiting position, as they have already been accepted for a job or
because they plan to enrol in an education or training course. Almost half of NEET
youths (46.9 %) are actively looking for work and would therefore match the classic
definition of being unemployed. Another group of NEET youths (22.4 %) would basically
like to work, but is not actively looking for a job. One can assume that they have given up
looking for work as a result of negative experiences or the perceived lack of perspectives
in the labour market. Another explanation might be that they have child care
responsibilities for a small child and intend to work in some years only when their child
will attend kindergarten or school. Finally, 21.5 % of NEET youths state that they are
neither looking for a job nor want to work, citing care responsibilities as the main
reason. The differentiation also shows that the unemployment indicator is too narrow,
7
as it would only register half of the NEET youths. In turn, not all NEET youths are at risk
of social exclusion, for example if they are in a waiting position or on parental leave.
4. Socio-structural characteristics of NEET youth
Table 3 shows the NEET status in dependence of socio-structural characteristics.
Relevant differences (more than 10 percentage points) result from migration
background, citizenship, care responsibilities and early school leaving. In case of young
people with migration background (first generation), the possibility of being in a NEET
situation is 19.4 %; the risk of those, who do not belong to this group, is only 6.7 %. Even
bigger are the differences for young people who do not have Austrian citizenship or
citizenship of another EU25 country. The greatest differences are in respect of early
school leaving. The risk of NEET status in case of early school leavers stands at 47.0 %,
i.e. almost every second early school leaver is in a NEET situation. For young people with
higher levels of education than compulsory school, the risk is only 4.6 %.
At 14.4 %, a remarkably higher NEET risk is also shown for youths, whose
parents only have a compulsory schooling certificate. Also worth mentioning are the
differences according to size of location and age (higher risk in cities and in case of older
youths). There are hardly any differences in respect of gender. Differentiating in
accordance to age, the group of 20 to 24-year olds shows a slightly higher risk with
females (11.8 % to 8.6 %), whilst younger cohorts do not show any gender differences
(risk females and males 6.3 % to 6.5 %).
8
Table 3: NEET status depending on socio-structural characteristics
Socio-structural
characteristic
Gender
Migration background
(first generation)
Citizenship of Austria
or another EU25
country
Lives in urban area
(town (population
31,000 or above)
Age (20 years and
older)
Education of parents
(only youths living
with parents)
Early school leaving
Child up to 3 years in
household
Characteristic
attribute
Male
Female
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
No
Yes
Total
max. compulsory
school
Apprenticeship/BMS
A-levels
University
Total
No
Yes
Total
No
Yes
Total
No
NEET status
Yes
Total
92.3
90.5
91.4
93.3
80.6
91.4
76.1
92.9
91.4
92.7
88.7
91.4
93.6
89.8
91.4
Row percent
7.7
9.5
8.6
6.7
19.4
8.6
23.9
7.1
8.6
7.3
11.3
8.6
6.4
10.2
8.6
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
NEET Yes
Column
percent
50.5
49.5
100.0
66.8
33.2
100.0
24.5
75.5
100.0
56.5
43.5
100.0
31.6
68.4
100.0
85.6
14.4
100.0
24.7
93.1
95.4
96.5
93.2
95.4
53.0
91.4
92.8
63.8
91.4
6.9
4.6
3.5
6.8
4.6
47.0
8.6
7.2
36.2
8.6
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
52.7
16.7
5.9
100.0
48.8
51.2
100.0
79.3
20.7
100.0
Only data from 2006 to 2011 are included in the analyses.
A higher NEET risk of a group is often wrongly interpreted because it is assumed that
NEET youths are primarily made up of this group. The fact for example, that youths with
migration background have a higher NEET risk, leads to the conclusion that most NEET
youths have a migration background. This conclusion is incorrect as the group of young
people with migration background with a share of 14.7 % of all 16 to 24-olds is overall
fairly small. To avoid such wrong conclusions, we have also included the column percent
in Table 3, which describe the socio-structural recruitment of NEET youths. The
migration background reveals that the majority of youths, i.e. 66.8 % do not belong to
the first generation. The same applies to the lack of Austrian citizenship or that of
another EU25 country. 75.5 % of NEET youths have Austrian or EU25 citizenship.
9
Finally, it should also be noted that the analysed socio-structural characteristics only
in parts have a direct impact on the NEET risk. Migration background, citizenship, age
and the size of the location only have an indirect effect mediated by other variables (see
the following path analyses).
5. Causes of NEET
To identify factors, which have an impact on the occurrence of NEET status, an
explorative path model was analysed―separated into female and male youth―, as it was
assumed that different influencing factors would apply to young women and men (see
Section 3 above). In order to assess the influence of care responsibilities, we had to
dispense with taking the social origin into account―operationalized via the parent’s
achieved formal educational level―, as information on parents is only known if they live
in a joint household with these young adults. This is often no longer the case with young
people who have care responsibilities. In order to consider the causal structure,
according to which the cause must have taken place before the effect occurred, the
measurements of the socio-structural variables (URBAN, MIGRA (first generation),
CITIZ, AGE), early school leaving (EARLY) and health-related impairments (HANDI)
refer to the first panel wave (t1), unemployment experiences (UNEMP) and care
responsibilities towards children under age three (CHILD3Y) to the second wave (t2)
and the NEET indicator (NEET) to the third wave (t3). Apart from statistical significance,
only those path coefficients (= standardised partial regression coefficients), whose
absolute value is larger or equivalent to 0.1, were regarded as meaningful.
Figure 1 depicts the empirical path model of female youths. The NEET risk is
directly dependent on four factors. It increases as a result of care responsibilities
towards small children, previous unemployment experiences, early school leaving and
illnesses resp. impairments. Looking at the path coefficients, one can see that early
school leaving (.25) and care responsibilities (.25) have the strongest direct influence on
the NEET risk. Migration background, size of location, age and citizenship―as already
mentioned―do only play a secondary role, as they only have an indirect effect on the
NEET risk via direct influencing factors.
Of particular relevance is the influence of early school leaving, as this factor also
has an indirect impact on the NEET risk, via an increased unemployment risk and care
responsibilities. The strong influence of early school leaving on unemployment
experiences is consistent with the statement of Gieseke et al. (referring to the German
labour market) (2010, 435) that on the labour market “marginalisation and exclusion
risks―sometimes even stronger than in the past―are still sustainably influenced by
school and in particular vocational certificates“ (translated by the authors). 8 Lassnigg
(2010) as well as Reckinger (2010) came to a similar conclusion for the Austrian labour
market. The increased possibility of care responsibilities towards small children due to
Regarding further papers, which explicitly deal with the consequences of leaving the education system
prematurely for labour market opportunities, reference is made to Solga (2009) and for Austria to NairzWirth and Mesching (2010) as well as to Steiner (2011).
8
10
early school leaving has also been empirically proven. Nakhla et al. (2012) report for
Germany that poor school performance and leaving school prematurely are key risk
factors for teenage pregnancies.
Early school leaving occurs more frequently in case of female youths, if these do
not have Austrian or EU25 citizenship. This is far more likely if the young person
belongs to the first generation of migrants (see also Steiner 2009, 148). With regards to
our findings and those of Steiner (2009) one has to put into perspective that the effects
were not controlled in accordance with social class. Due to existing findings on
migration-related inequalities of labour market opportunities (see e.g. Granato & Kalter
2001; Kalter 2008; Krause & Liebig 2011; Smoliner 2011; Stadler & Wiedenhofer-Galik
2011) and on education-related disadvantages of migrants (see e.g. HerzogPunzenberger 2012), one can assume that a significant part of the migration effect on the
early school leaving risk can be explained by the social situation of families with
migration background.
The direct influence of care responsibilities on the NEET risk must be seen
against the background of presumably still firmly enshrined traditional gender
stereotypes, which direct particularly low-qualified young mothers to the reproductive
area of family work and child care, as German studies show (see e.g. Mühling et al. 2006;
Trappe et al. 2009). A lack of nursery places for babies and toddlers should also be
mentioned as a further cause (see e.g. AK-Frauenmonitor 2013).
The socio-structural variables of age, size of location and migration background
(= belonging to the first generation) have an indirect impact, which means that these
variables only have an effect on the NEET risk in via other factors, such as early school
leaving.
11
Figure 1: Results of the explorative path analysis of the NEET risk of young women
Only data from 2006 to 2011 are included in the analyses.
For the meaning of the variables please see Table 1.
Compared to female youths, the NEET risk of young men can be explained to a lesser
degree by the specified path model (see Figure 2). The explained variance amounts 15
%, whilst a value of 26 % is shown for young women.
If one also includes the number of vacancies per 1,000 employees as an indicator
for the economic situation and the expenditure for active labour market policy as
context characteristics at federal state level, one can see that both context
characteristics have a significant influence in case of young men, whilst they have no
impact on young women. This can be taken as an indication that young men are more
dependent on both the economic situation and the active labour market policy than
young women. This can be explained by the fact that young men with low qualifications
are more often than young women employed in sectors, which are dependent on the
economic situation, such as the construction industry.
At the individual level (see Figure 2)―analogous to female youths―direct
influences of early school leaving, previous unemployment and ill health resp.
impairments occur. In contrast to young women, unemployment experience has a
greater impact than early school leaving. However, in respect of young men, care
responsibilities towards children under three have no influence on the NEET risk.
12
In contrast to young women, early school leaving and the occurrence of illnesses
resp. impairments correlate positively. However, a directed link cannot be clearly
determined on the basis of the present measures. It is possible that ill health in case of
male youths frequently results in them leaving school early. At present, no reliable
empirical findings exit.
As with female youths, the socio-structural characteristics only indirectly affect
the NEET risk.
Figure 2: Results of the explorative path analysis for the NEET risk of young men
Only data from 2006 to 2011 are included in the analyses.
For the meaning of the variables please see Table 1.
The results of the specified path models for male and female youths can only explain
part of the NEET risk. The complexity of NEET causes only becomes apparent in
combination with qualitative research results. Let’s give an example: Sibel (not her real
name, case example shown in Figure 3), was born in Austria and 18 years old at the time
of the survey. Both parents have immigrated from Turkey. Sibel lives in central
Vorarlberg. Her parents divorced when she was 12. Sibel has a relatively
underprivileged educational background (parents and elder siblings have not
undergone vocational training). In the beginning, poor knowledge of German made it
difficult for Sibel to follow her lessons. She repeated the 4th class of primary school
(ISCED 1). Due to her ethnicity, Sibel has experienced exclusion and stigmatisation. She
13
finished the 4th class of secondary school (ISCED 2) with an “unsatisfactory” in English,
among other because she missed the examination date due to a family vacation. Letters
of application and taking part in several “taster internships” are not successful. Poor
social and cultural resources, a lack of problem awareness (in view of acquisition of
education) and a poor school qualification as a result, can all be identified as main
reasons for failing in the labour market. Since leaving school, Sibel has been in a twoyear floating status between NEET and temporary jobs (catering, production) and job
creation measures. At the time of the survey, Sibel is taking part in a youth employment
scheme and displays high motivation to find a permanent job (auxiliary work) once she
has completed the course. However, the desire to start an apprenticeship to improve her
professional qualifications is shelved due to the lack of success in finding an
apprenticeship and her meanwhile resigned attitude. She reports of 50 to 60
applications for an apprenticeship, for most of which she did not even get a reply.
The causes identified in the qualitative interviews can be combined to two
dimensions (Stadlmayr & Lankmayer 2013). One dimension describes the attitude of the
person affected towards his or her NEET status; the other describes the existing
individual conditions, manifesting social inequalities of opportunities. Based on these
two dimensions, it is possible two determine five main types (for further details see
Stadlmayr & Lankmayer 2013).
The linkage to the quantitative results can be described in the following way: The
factor “failure in acquiring education” reflects the direct influence of early school leaving
on NEET in the path analysis of females (see Figure 1). The factor “failure to find
apprenticeship” corresponds to the variable previous unemployment. The qualitative
analysis explores the causes that results in these two factors. Additionally it refers to
necessary competencies for a successful entry in the labour market.
14
Figure 3: Case example Sibel ‒ failure to acquire education, no success in the
labour market
6. Factors for exiting NEET
In view of developing recommendations for action it seems to be relevant, what the
decisive factors are for a successful exit from NEET. The identification of these factors is
based on a multivariate analysis. Overall―dependent on the definition (Bacher et al.
2013) ―32 % to 47 % succeeded in exiting a NEET situation.
The analyses show that female NEET youths succeed in exiting if they actively
look for work and live in urban areas (Bacher et al. 2013). The influence of the residence
(URBAN) can possibly be explained by the fact that childcare facilities are more
widespread in cities. Another explanation could be that urban areas provide more job
opportunities for young women. However, on the other hand, AGE (between 20 and 24),
care responsibilities (CHILD3Y) und early school leaving (EARLY) make exiting NEET
more difficult. Additional calculations show that in case of female NEET youths, contacts
established through job creation measures (JOBSEARCH) have a significant positive
influence on exit.
The picture looks quite different in case of male NEET youths. Relevant factors
for their successful exit are the existence of school-leaving qualifications, which go
beyond those of a compulsory school as well as a lack of illnesses/impairments (HANDI).
In contrast to female NEET youths, taking part in a course has no positive effect for
young men. However, it has to be emphasised that based on the data used it has only
been possible to analyse short-term measures. Nonetheless, Lutz and Mahringer (2007)
15
arrive at a similar result. The authors were able to demonstrate that women aged
between 25 and 44, benefit more from placement support measures and qualifications
than men. The high level of effectiveness is partly a result of measures, which were
especially developed and used for problem situations for females; e.g. women reentering the labour market.
At the context (province) level, a positive effect of the economic situation on
successfully overcoming the NEET situation can be identified for young men (Bacher et
al. 2013). In contrast to young women, it is easier for male NEET youths to exit NEET in
times of an economic upswing, which―as already mentioned (see Section 5)―can be
explained by the fact that they are more frequently employed in industries that are
dependent on economic trends.
Table 4: Factors influencing the exit of NEET separated by gender (linear models)
Female NEET youths
b
Beta
Male NEET youths
p
b
<.001
.944
Beta
Difference
p
z-value
p
<.001
-1.751
.086
Constant
.686
HANDI
-.090
-.059
.206
-.285
-.213
<.001
1.885
.067
MIGRA
-.095
-.093
.176
-.006
-.005
.941
-.832
.282
AGE
URBAN
-.143
.106
-.121
.103
.014
.031
-.084
-.025
-.087
-.026
.109
.638
-.759
1.827
.299
.075
CRISIS (2009/10)
.009
.009
.860
.029
.030
.592
-.272
.384
YEAR2007
-.002
-.001
.980
.043
.037
.518
-.496
.353
EARLY
CITIZ
-.307
.014
-.308
.014
<.001
.841
-.351
.004
-.374
.003
<.001
.967
.619
.094
.329
.397
JOBSEARCH
.245
.233
<.001
-.039
-.041
.447
3.730
<.001
CHILD3Y
-.205
-.198
<.001
.049
.019
.726
-1.696
.095
R²
.304
.207
Only data from 2006 to 2011 are included in the analyses.
For the meaning of the variables please see Table 1.
In addition to the variables specified in Table 1, the time of the survey was also included.
For this purpose, dummy variables (CRISIS) were created for crisis years 2009 and 2010
and for the unexplainable increase in 2007 (YEAR2007; see Table 2).
Which factors favour a successful exit from NEET, depends on the individual
situation, which can only be partially depicted by the underlying statistical analyses.
Qualitative interviews show that in particular the following three factors are relevant for
the success of “exit measures”:
(i)
(ii)
They have to be applied in a differentiated manner and correspond to the
environment resp. the overall situation of the young people.
They have to be fine-meshed and continuous―a combination of various parts
to cover complex requirements and coordination by means of case
management―to avoid anybody from “dropping out”.
16
(iii)
They should put youths at the centre, take their opinions and needs seriously,
meet them on an equal footing and encourage participation (reassurance,
empowerment, self-determination).
In conversations with youths in NEET situations, who are actively looking for
employment, one detects a need for support in the application process as well as for an
appropriate career choice. An occupational choice, which, following an adequate
orientation phase has been made consciously and independently, can be more
sustainable than a pragmatic and/or heteronomous occupational choice. Apart from an
appropriate career choice, support as to how to apply for a job is also helpful. Besides
conveying application know-how (writing letters of application and a CV, conduct during
job interviews etc.), above all a permanent emotional (encouragement, understanding)
and motivational (as regards active application, contacting organisers of job-creating
programmes) support of young people is of vital importance. These findings correspond
to the international current state of research (Tunnard 2008; Maguire & Thompson
2007; Simmon & Thompson 2011; Tanner et al. 2007).
Whether consultations and measures will be effective, is very much dependent on
the relationship between consultant and youth. Of equal importance are sufficient
resources and qualifications of the advisory or coaching staff to be able to address a
wide range of requirements and if necessary, to compensate for inadequate support
provided by family or social environment.
The desire for fair opportunities runs like a common thread through the interviews
with NEET youths. Some young people are denied an apprenticeship or a job because of
a lack of formal qualification (and not corresponding to the prevailing performance
standard), which means they do not get the opportunity to prove their skills and talents.
7. Summary and conclusions
This article explored the questions (i) how many young people in Austria are affected by
a NEET status, (ii) which socio-structural characteristics they display and (iii) what the
causes of an increased NEET risk are? It has also been investigated (iv) on which factors
a (successful) exit from the NEET situation depends.
Ad (i) The results show that on average about 78,000 youths were in a NEET
situation in the period from 2006 to 2012. This is equivalent to 8.5 % of all young people
aged between 16 and 24 (NEET rate), whereby their share resp. their number strongly
depends on economic trends.
Ad (ii) Depending on socio-structural characteristics, early school leavers,
(female) youths with care responsibilities, youths of the first generation and youths
without EU25 citizenship have a higher risk of having NEET status. The same applies to
youths, whose parents have a lower formal level of education. A higher NEET risk can
also be observed in urban areas and in case of young people aged between 20 and 24.
However, the socio-structural characteristics should not be interpreted as if NEET
17
mainly affects migrants. For example, 75.5 % of NEET youths have Austrian or EU25
citizenship. Furthermore, the socio-structural characteristics have only an indirect
impact on the NEET risk.
Ad (iii) The path analyses help to explain the NEET risk of young women through
care responsibilities, leaving school prematurely, previous unemployment and ill
health/impairments, whereby care responsibilities and early school leaving exercise the
strongest direct influence. In case of male NEET youths, early school leaving, previous
unemployment and ill health have a direct impact on the NEET risk. Early school leaving
as central cause for a NEET situation is also confirmed by the qualitative survey. This is
added in case of male youths by the economic situation and money spent on active
labour market policy. The likely explanation for this is that male youths with poor
qualifications are more frequently working in industries which are strongly dependent
on the economic situation.
Ad (iv) A successful exit has been achieved by 32 % to 47 % of NEET youths. In
view of the short observation period of five quarters it cannot be definitely said whether
this exit can be sustained. Relevant factors for an exit by female NEET youths are schoolleaving qualifications beyond compulsory school, active job search, size of location and
participation in training measures. An active job search, living in an urban area and
taking part in course facilitate an exit. Relevant factors for an exit in case of male NEET
youths are primarily the lack of illnesses/impairments and school-leaving qualifications,
which go beyond those of a compulsory school. This is added by a context effect of the
economic situation.
Even though Austria has one of the lowest NEET rates in an international
comparison, a further reduction should be aimed. At individual level, the NEET status
impairs a self-determined life; at social level it incurs considerable economic costs.
However, in particular the negative socio-political consequences (Eurofound 2012)
suggest political measures and strategies for a further reduction.
In accordance with the path analyses, measures could aim at changing the value of a
direct influencing factor, for example by lowering the share of early school leavers.
However, measures also can - which is often ignored - aim at decreasing the relation
between an influence factor and the NEET rate, hence by reducing the link between
early school leaving and the NEET rate, by offering jobs specifically to early school
leavers. Based on this initial consideration, one can formulate the following sub-targets,
which, when realized, can achieve a reduction of the NEET rate resp. the number of
NEET youths:
•
Reduction of the share of early school leavers and/or reduction of the relation of
early school leavers and NEET
•
Reduction of illnesses/impairments and/or the relation of illnesses and NEET
•
Reduction of unemployment experiences and/or the relation of unemployment
experiences and NEET
•
Reduction of the relation of early pregnancy and NEET
18
Taking account of the fact that NEET status - as the qualitative interviews have
impressively demonstrated - is based on an individual chain of causes, one can derive
the following as an initial recommendation for action:
Measures shall take the individual needs of young people into account;
they shall have a low-threshold, be comprehensive and flexible,
intervene in time and have a lasting effect.
The youth coach, who was introduced in Styria and Vienna in 2012 and throughout all of
Austria in 2013 does to a large extent fulfil this request. The aim of youth coaching is to
reach young people at risk of social exclusion at an early stage and if required prevent
premature school leaving through consultation, support or case management
(Bundessozialamt 2011; see Info box 1). The relationship between consultant/coach and
young person is vital to achieve success. Hence, sufficient resources and qualifications
resp. further training of youth coaches are of great importance. However, a deficit, which
ought to be remedied, is the fact that starting youth coaching as a preventive measure
only from the 9th grade, is relatively late. It is recommended to start this measure earlier,
for example in the 7th or 8th grade and also to extend the period of aftercare (Steiner et
al. 2013) and to raise the age limit9. This request could also be met by school social
work, outreach social work and youth work.
Info box 1: The Austrian model of the youth coach
Youth coaching was introduced in cooperation by the Federal Ministry for
Education, the Arts and Culture (BMUKK) and the Federal Ministry of Labour,
Social Affairs and Consumer Protection (BMASK) and implemented in the
provinces by the Federal Social Office (BSB). Youth coaching starts in the last
year of compulsory school (9th grade) trying to prevent young people from
leaving school prematurely. Based on an early warning system, where the
school system identifies pupils with a high risk of leaving school early, youth
coaches contact them and offer their support. This creates a cross-cutting
structure between education and labour market policy. Depending on
requirement, the voluntary offer includes consultation, support and
assistance respect of education, career plans and personal problems. An
intensified career choice shall enable young people to make independent
education and job-related decisions. If required, there is also the option of
case management until integration in the labour market or a successorsystem (BMASK 2013a, 84f.). The evaluation of youth coaching in the pilot
states Styria and Vienna shows a success rate of 85 %. This means that 15 %
terminated the scheme or left it without clear goal orientation. Effects of
youth coaching can be observed in particular in respect of career choice,
The target group currently includes pupils of the 9th grade at school and youths who are not in education
under 19 years of age as well as youths with (previous) special educational needs resp. disability up to 25
years of age (Bundessozialamt 2011, 8).
9
19
motivation and self-perception (Steiner et al. 2013, 170). In 2013, when
youth coaching was introduced as transition management throughout
Austria, € 22.3 million had been made available for about 35,000 participants
(BMASK 2013b, 216).
The results of our analyses would suggest the following additional recommendations for
action:
Developing measures for (new) target groups
Here one should name the first migration generation, mothers interested in further
education and apprenticeship graduates in the rural sector. Additional analyses that
focus on migrants of the first generation (Bacher et al. 2013) have shown that in
particular youths, who enter the country between their 15th and 18th year, have an
increased NEET risk. In many cases they do not enter the education system as they have
emigrated after the compulsory school period. At the same time, they often find it
difficult to access the labour market. With regard to young mothers in a NEET situation
it appears to be important to provide public childcare for the under threes. This can help
young mothers who left school early to finish their education or to start an
apprenticeship; this makes it easier to reconcile work and family life, which can
contribute to overcome NEET status. Public investments to expand the childcare
infrastructure would have a significant impact on employment and would pay off within
only four years (Buxbaum & Pirklbauer 2013). In order to improve the labour market
chances of apprenticeship graduates in rural areas, existing mobility programmes
should be evaluated as regards their impact and improved. Another strategy is to create
affordable housing for young people in urban areas.
The wide range of needs and problems of NEET youths require the cooperation of
relevant actors at regional level. Hence, the provision of a further recommendation for
action:
Measures are required that serve to network and coordinate relevant
actors at regional level (and strengthen the region).
We would recommend youth networks10 between school, youth welfare (in the area of
school social work), Federal Social Welfare Office, youth coaches, job centre, employers,
lobby groups, further training institutions, leisure facilities, NGOs, regional medial
insurances and other actors responsible for healthcare. The focus should be on
conveying knowledge (via individuals and organisations), lasting and equal contacts and
the common target of (labour market) integration of youths. Networking structures have
also been provided in the concept of youth coaching (Bundessozialamt 2011, 28f.). Apart
from that, the integration of young people themselves seems to be important (Butt-
Successful structures for this can be found in Salzkammergut, more detailed information under
http://media.arbeiterkammer.at/ooe/bezirksstellen/gmunden/F_2012_Jugendnetzwerk_Salzkammergut.
pdf.
10
20
Posnik 2012). This promotes the democratic competencies of young people and has the
ability to increase the acceptance of measures.
Finally, the following measures are also required:
Measures at regional and national level, which provide adequate legal,
financial and organisational framework conditions
Courses of action exemplary for active labour market and employment policy shall be
shown. Additional courses of action to lower the NEET rate can be found in Tamesberger
(2013).
Active labour market policy can contribute to achieving the previously mentioned
sub-targets, by for example offering programmes for unemployed early school leavers.
As the latter often have negative learning experiences, in particular the combination of
practical work experiences and qualifications (e.g. production schools) has proven to be
successful. These alternative forms of learning to stabilise young people and to learn
social and education-related skills should be developed and increasingly offered in
combination with outreach, open youth work. An important aspect of active labour
market policy for youths is the training guarantee (see Info box 2), which provides
unsuccessful apprenticeship applicants with supra-company vocational training. Based
on present findings, one can derive the recommendation that the training guarantee for
the target group of 20 to 24-year olds should be expanded and that young adults should
be given an age-appropriate, second chance to vocational training. More than two thirds
of NEET youths (68.4 %) belong to this age group.
Info box 2: Training guarantee
The Austrian Training guarantee has been in place since year of training
2008/2009 for youths up to the age of 18. The key aspect of the training
guarantee is supra-company vocational training, which, due to the falling
number of company-based apprenticeships had been introduced in Austria
as early as 1998. Following a range of legal changes, a uniform training
framework of supra-company vocational training does now exist, which
enables high value training until graduation. There are two types of supracompany vocational training. Firstly, the full completion of the
apprenticeship (ÜBA 1), which is carried out in training facilities or in
cooperation with company training workshops. Secondly, a shorter
apprenticeship (ÜBA 2), which takes place in training facilities in cooperation
with companies. Supra-company vocational training targets young people
registered with the job centre who were unsuccessful in finding a companybased apprenticeship and those who broke off a company-based
apprenticeship. In year of training 2012/2013, € 175 million were
earmarked for 11,717 supra-company vocational training places (BMASK
2013b, 209f.). Another form of apprenticeship is integrated vocational
education (IBA). It enables youths with special educational needs, with
21
secondary modern school qualification or disabled persons to complete a
partial apprenticeship (partial qualification) or an extended apprenticeship
(up to two years) in a company. During their training, apprentices will be
supported by vocational training assistance. In 2012, 5,741 youths took part
in integrated vocational education (BMASK 2013a, 121ff.)
On the one hand, it is necessary to create more job opportunities for young people with
health problems, social-emotional issues and/or low level of education (second labour
market). The key social challenge - both in Europe and in Austria - is the shortage of
jobs. Here, the public sector could assume the function of an “employer of last resort”
(see Tcherneva 2012). This would correspond to the desire by the young people who
were interviewed, to get a fair chance in the world of labour, independent of their level
of education.
Finally we would like to point out the danger of stigmatisation through the NEET
indicator and recommend a sensible handling in respect of public communication. Both
international literature (Gracey & Kelly 2010; House of Commons 2010) and youth work
practitioners refer to this aspect. As the article has shown, it is important to emphasise
that NEET youths are a very heterogenic group, which has very different problems and
needs. One should also warn against placing the causes for a NEET situation primarily
within individuals. The actual reason is structural causes, which have to be seen in the
context of economic, social and political circumstances.
22
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Authors
Johann Bacher, Head of Department of Empirical Social Research at the Institute of
Sociology at Johannes Kepler University Linz.
Dennis Tamesberger, Assistant for Labour Market Policy at the Department for
Economic, Welfare and Social policy at Chamber of Labour Linz.
Heinz Leitgöb, Research Associate at the Department of Empirical Social Research at the
Institute of Sociology at Johannes Kepler University Linz.
Thomas Lankmayer, Member of Staff at Research Institute for Vocational Training and
Adult Education Johannes Kepler University Linz.
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